The present invention relates generally to data processing systems and methods, and more particularly to systems and methods for identifying statistically linear data in a data set of an amplification process, such as polymerase chain reaction (PCR).
Many experimental processes exhibit amplification of a quantity. For example, in PCR, the quantity may correspond to the number of parts of a DNA strand that have been replicated, which dramatically increases during an amplification stage or region. Other experimental processes exhibiting amplification include bacterial growth processes. The quantity is detected from an experimental device via a data signal, whose data points are analyzed to determine information about the amplification. As part of the data analysis, it is important to know if amplification has potentially occurred; otherwise, effort might be wasted on analyzing non-amplifying data. If the data is statistically linear, then amplification has not occurred.
Ideally, the data from the amplification detection device would be a monotonic and continuous signal, thus one could easily identify whether the data, or portions thereof, has statistically linear behavior. However, the signal from the amplification device typically contains noise, thus making identifying a behavior of the signal difficult. The noise manifests itself in each data point in the signal from the device having random fluctuations that occur on top of the true signal, e.g. the actual number of DNA strands. Thus, the data requires processing to allow for identifying of linear behavior.
A typical prior method for processing data to determine if it is statistically linear is with a linear least squares (LSQ) fit. The correlation value of the LSQ fit can be used to determine whether there is an adequate fit. By standard convention, a correlation value of 0 is related to a bad fit, thus the data is not linear, and a value of 1 suggests a good fit for linearity. The problem is that in the presence of noise, the correlation value can be close to 0 or 1 for data that looks statistically linear. Additionally, the correlation value does not correspond to a physical value that may provide additional insight and efficacy. Thus, the correlation value is not an acceptable criterion, particularly for data that can be extremely noisy.
Therefore it is desirable to provide systems and methods for processing a data set having noise, and for identifying whether the dataset is statistically linear, that overcome the above and other problems.